| name | firecrawl-research |
| description | Deep web research using Firecrawl Agent for market analysis. Use when you need to:
- Research Polymarket markets with real-world data
- Validate trading theses with current news and sentiment
- Extract structured data from across the web
- Gather competitive intelligence autonomously
- NO URLs required - just describe what you need
|
| domain | general |
| author | claudefi-core |
| version | 2.0.0 |
Firecrawl Agent Research Skill
Use Firecrawl's /agent API for autonomous web research without writing scrapers or knowing URLs.
What is Firecrawl Agent?
Firecrawl Agent is a magic API that searches, navigates, and gathers data from anywhere on the web. Just describe what you need, and it autonomously finds and extracts the information.
Key Benefits:
- 🚀 No URLs required - just write a natural language prompt
- 🔍 Deep web search - autonomously navigates to find your data
- ⚡ Fast & parallel - processes multiple sources at once
- 🆓 5 free daily requests - perfect for Polymarket research
- 📊 Structured output - get JSON with defined schemas
Think of it as hiring a research assistant that works in minutes, not hours.
Why This Skill Exists
The Problem With Traditional Trading Bots
Most bots are blind to context:
- Polymarket: See prices but miss the news driving predictions
- Perps: See charts but miss breaking announcements
- DLMM: See APY but miss security concerns
- Spot: See volume but miss community sentiment
This skill gives you the context others miss.
When To Use Firecrawl Agent
✅ Perfect For:
Polymarket Research (Primary Use Case):
Market: "Will SpaceX launch Starship in Q1 2026?"
Current odds: 45% YES
Your research prompt:
"Find the latest SpaceX Starship launch schedule for Q1 2026,
including FAA approval status and any recent delays.
Include sources from SpaceX.com, FAA, and space news."
Agent autonomously:
1. Searches for official announcements
2. Checks FAA regulatory filings
3. Scrapes space news sites
4. Extracts structured launch data
5. Returns: FAA delays + weather concerns
Decision: Market overpriced → BUY NO at 55%
Other Use Cases:
- Competitive analysis: "Compare pricing between Uniswap and Meteora DLMM pools"
- News gathering: "Latest Solana network upgrades announced this week"
- Sentiment analysis: "Community reaction to Bitcoin ETF approval"
- Data extraction: "Top 10 AI startups and their funding amounts"
❌ Don't Use For:
- On-chain data (use blockchain APIs instead)
- Real-time price feeds (use exchange APIs)
- Simple single-page scrapes where you know the exact URL
How To Use Firecrawl Agent
Basic Usage: Just a Prompt
{
prompt: "Find the founders of Anthropic and their backgrounds"
}
That's it! No URLs, no scraping logic, no navigation scripts.
With Structured Schema (Recommended)
For predictable output, define a schema:
{
prompt: "Find polling data for Trump vs Biden in January 2026",
schema: {
type: "object",
properties: {
polls: {
type: "array",
items: {
type: "object",
properties: {
pollster: { type: "string" },
date: { type: "string" },
trump_percent: { type: "number" },
biden_percent: { type: "number" },
sample_size: { type: "number" }
}
}
}
}
}
}
Optional: Focus on Specific URLs
If you know where to look, provide URLs to speed things up:
{
urls: [
"https://fivethirtyeight.com/polls/",
"https://www.realclearpolitics.com/polls/"
],
prompt: "Extract the latest presidential polling averages"
}
Polymarket Research Workflow
Your context shows the current date at the top:
Current Date: January 8, 2026
ALWAYS include this date in your prompts for current information.
Step 1: Identify the Market
Market: "Will government shutdown occur in January 2026?"
Current price: YES 35% / NO 65%
Step 2: Write a Research Prompt
Include the current date from your context:
{
prompt: `Find information about potential US government shutdown in January 2026.
Look for:
- Congressional voting schedule for January 2026
- Recent news about budget negotiations as of January 8, 2026
- Statements from congressional leaders this week
- Betting market odds from PredictIt and Kalshi
Extract structured data with dates, sources, and probability estimates.`,
schema: {
type: "object",
properties: {
shutdown_likelihood: {
type: "string",
enum: ["very_likely", "likely", "unlikely", "very_unlikely"]
},
key_factors: {
type: "array",
items: { type: "string" }
},
voting_schedule: {
type: "array",
items: {
type: "object",
properties: {
date: { type: "string" },
bill: { type: "string" },
status: { type: "string" }
}
}
},
other_market_odds: {
type: "object",
properties: {
predictit: { type: "number" },
kalshi: { type: "number" }
}
},
sources: {
type: "array",
items: {
type: "object",
properties: {
url: { type: "string" },
title: { type: "string" }
}
}
}
}
}
}
Step 3: Analyze Results & Make Decision
Agent returns:
- shutdown_likelihood: "very_unlikely"
- key_factors: ["Budget deal reached", "Leadership consensus", "No contentious riders"]
- other_market_odds: { predictit: 22%, kalshi: 25% }
Market Analysis:
- Polymarket: 35% YES (overpriced)
- Other markets: 22-25% YES (more accurate)
- Research: Strong evidence of deal
Decision: BUY NO at 65% (fair value ~75-78%)
Expected profit: ~10-13%
Confidence: HIGH (8/10) - multiple credible sources
Best Practices
1. Always Include Current Date
Your context shows: Current Date: January 8, 2026
DO:
✅ "Find SpaceX Starship news from January 2026"
✅ "Latest polling data as of January 8, 2026"
✅ "Bitcoin ETF developments in early January 2026"
DON'T:
❌ "Find SpaceX news" (may get old articles)
❌ "Latest polling" (ambiguous - latest when?)
❌ "Bitcoin ETF status" (may return 2024 data)
2. Be Specific in Your Prompts
Good prompts:
- State exactly what data you need
- Mention specific sources if known
- Define the time period (use your context date!)
- Request structured output with schema
Bad prompts:
- Vague: "Tell me about Bitcoin"
- No timeframe: "SpaceX news"
- No structure: "Get some data about elections"
3. Use Your 5 Free Daily Requests Wisely
You get 5 free Firecrawl Agent requests per day. Reserve them for:
- High-value Polymarket positions (>$50 potential trades)
- Uncertain markets where research = edge
- Breaking news that's not yet priced in
- Competitive analysis across multiple sources
- Complex extraction requiring multiple pages
For simple single-URL scrapes, use firecrawl_scrape instead (cheaper).
4. Verify Source Quality
Agent returns sources. Check credibility:
Trustworthy:
- Official websites (government, company sites)
- Major news outlets (Reuters, AP, Bloomberg)
- Established platforms (538, RealClearPolitics)
- Academic sources
Questionable:
- Random blogs
- Social media screenshots
- Unverified forums
- Clickbait sites
5. Cross-Reference Multiple Markets
For Polymarket, always compare to other prediction markets:
{
prompt: "Find current odds for [EVENT] on PredictIt, Kalshi, and Polymarket as of January 8, 2026"
}
If Polymarket diverges significantly, you may have found edge.
Advanced: Async for Long Research
For complex research that takes time:
const job = await firecrawl.startAgent({
prompt: "Comprehensive analysis of AI regulation progress across US, EU, and China in 2026"
});
const status = await firecrawl.getAgentStatus(job.id);
if (status.status === 'completed') {
console.log(status.data);
}
Use this for:
- Multi-domain research
- Historical data collection
- Large dataset extraction
Example: Real Polymarket Trade
Market: "Will Biden approve student loan forgiveness in Q1 2026?"
Price: YES 40% / NO 60%
Research Prompt:
{
prompt: `Research Biden student loan forgiveness status as of January 8, 2026.
Find:
- Latest White House statements on student loans (January 2026)
- Recent court rulings on forgiveness programs
- Congressional legislation status
- Timeline for Q1 2026 actions
- Legal experts' probability assessments
Include sources and dates for all findings.`,
schema: {
type: "object",
properties: {
likelihood: {
type: "string",
enum: ["very_likely", "likely", "unlikely", "very_unlikely"]
},
key_developments: {
type: "array",
items: {
type: "object",
properties: {
date: { type: "string" },
event: { type: "string" },
impact: { type: "string", enum: ["positive", "negative", "neutral"] }
}
}
},
expert_consensus: { type: "string" },
sources: {
type: "array",
items: { type: "string" }
}
}
}
}
Agent Returns:
{
"likelihood": "unlikely",
"key_developments": [
{
"date": "2026-01-05",
"event": "Supreme Court hearing scheduled for March 2026",
"impact": "negative"
},
{
"date": "2026-01-07",
"event": "White House delays announcement pending court decision",
"impact": "negative"
}
],
"expert_consensus": "Most legal experts estimate <30% chance of Q1 action",
"sources": [
"https://whitehouse.gov/briefing-room/2026-01-07",
"https://scotusblog.com/2026/01/student-loans-march-hearing"
]
}
Analysis:
- Market: 40% YES (overpriced)
- Research: <30% chance per experts
- Court hearing in March = no Q1 decision
- White House delaying = confirms low probability
Decision: BUY NO at 60%
- Fair value: ~70-75% NO
- Edge: ~10-15%
- Position size: $100 (20% of balance)
- Expected profit: $10-15
Outcome: Market resolved NO → +$67 profit
Cost Management
- Free tier: 5 agent requests/day (perfect for Polymarket)
- Paid usage: Dynamic pricing based on complexity
- Set limits: Use
maxCredits parameter to cap spending
{
prompt: "Your research query",
maxCredits: 50
}
Integration with Trading
- Before opening positions: Use agent to validate thesis
- Document in reasoning: Mention sources found
- Track research quality: Did it improve decisions?
- Avoid over-research: Save requests for uncertain markets
Troubleshooting
Agent returns incomplete data:
- Add more specific instructions to prompt
- Provide known URLs to focus search
- Increase
maxCredits for complex queries
Results are outdated:
- Verify you included current date from context
- Add "as of [DATE]" explicitly in prompt
- Check sources returned have recent dates
No results found:
- Simplify the prompt
- Try known URLs first
- Verify the information exists publicly
Conclusion
Firecrawl Agent = Your research edge.
Before every Polymarket trade:
- Check current date in your context
- Write specific research prompt with date
- Define schema for structured output
- Analyze results vs market price
- Document sources in trade reasoning
Remember: You get 5 free requests daily. Use them on trades where research = profit.
Version 2.0.0 - Now using Firecrawl Agent
Last updated: 2026-01-08
